import pandas as pd 
from datetime import datetime 

import numpy as np
import chart_studio.plotly as py 
import seaborn as sns
import plotly.express as px
import cufflinks as cf
%matplotlib inline
from plotly.subplots import make_subplots

import plotly.graph_objs as go
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
init_notebook_mode(connected = True)
cf.go_offline()
import warnings
warnings.filterwarnings('ignore')

Uploading data

I download the data from https://covid.ourworldindata.org/data

pd.set_option('display.max_columns', None)

url = 'https://covid.ourworldindata.org/data/owid-covid-data.csv'

parser = lambda x: pd.datetime.strptime(x, '%Y-%m-%d')

df = pd.read_csv(url, parse_dates=['date'], date_parser=parser)
df = df[df['date'] >= '2020-03-04']

Adding Year, Month, Day Column

df['year'] = [ x.year for x in df.date]
df['month'] = [x.month for x in df.date]
df['day'] = [x.day for x in df.date]

df['day_name'] = [x.day_name() for x in df.date]
df['week_nr'] = [x.isocalendar()[1] for x in df.date]
df['month_name'] = [x.strftime("%b") for x in df.date]

# Preparing the number of weeks of the pandemic
df.loc[df['date'] <= '2021-01-03', 'week_nr'] = df['week_nr'] - 9 
df.loc[df['date'] > '2021-01-03', 'week_nr'] = df['week_nr'] + 45 

df['fatality'] = df['total_deaths'] / df['total_cases'] 
df['fatality'] = df['fatality'] * 100
df['fatality'] = [round(x,2) for x in df['fatality']]
df = df.replace(np.nan,0)

Adding % infected, one dose/fully vacinated people

df['infected'] = round(df['total_cases'] / df['population'] *100,2)
df['vacinated'] = round(df['people_vaccinated'] / df['population'] * 100)
df['vacinated_fully'] = round(df['people_fully_vaccinated'] / df['population'] * 100)

df

	iso_code	continent	location	date	total_cases	new_cases	new_cases_smoothed	total_deaths	new_deaths	new_deaths_smoothed	total_cases_per_million	new_cases_per_million	new_cases_smoothed_per_million	total_deaths_per_million	new_deaths_per_million	new_deaths_smoothed_per_million	reproduction_rate	icu_patients	icu_patients_per_million	hosp_patients	hosp_patients_per_million	weekly_icu_admissions	weekly_icu_admissions_per_million	weekly_hosp_admissions	weekly_hosp_admissions_per_million	new_tests	total_tests	total_tests_per_thousand	new_tests_per_thousand	new_tests_smoothed	new_tests_smoothed_per_thousand	positive_rate	tests_per_case	tests_units	total_vaccinations	people_vaccinated	people_fully_vaccinated	total_boosters	new_vaccinations	new_vaccinations_smoothed	total_vaccinations_per_hundred	people_vaccinated_per_hundred	people_fully_vaccinated_per_hundred	total_boosters_per_hundred	new_vaccinations_smoothed_per_million	stringency_index	population	population_density	median_age	aged_65_older	aged_70_older	gdp_per_capita	extreme_poverty	cardiovasc_death_rate	diabetes_prevalence	female_smokers	male_smokers	handwashing_facilities	hospital_beds_per_thousand	life_expectancy	human_development_index	excess_mortality	year	month	day	day_name	week_nr	month_name	fatality	infected	vacinated	vacinated_fully
9	AFG	Asia	Afghanistan	2020-03-04	5.0	0.0	0.000	0.0	0.0	0.000	0.126	0.000	0.000	0.000	0.000	0.000	0.00	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000	0.000	0.0	0.000	0.000	0.0	0	0.0	0.0	0.0	0.0	0.0	0.0	0.00	0.00	0.00	0.0	0.0	27.78	39835428.0	54.422	18.6	2.581	1.337	1803.987	0.0	597.029	9.59	0.0	0.0	37.746	0.5	64.83	0.511	0.0	2020	3	4	Wednesday	1	Mar	0.00	0.00	0.0	0.0
10	AFG	Asia	Afghanistan	2020-03-05	5.0	0.0	0.000	0.0	0.0	0.000	0.126	0.000	0.000	0.000	0.000	0.000	0.00	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000	0.000	0.0	0.000	0.000	0.0	0	0.0	0.0	0.0	0.0	0.0	0.0	0.00	0.00	0.00	0.0	0.0	27.78	39835428.0	54.422	18.6	2.581	1.337	1803.987	0.0	597.029	9.59	0.0	0.0	37.746	0.5	64.83	0.511	0.0	2020	3	5	Thursday	1	Mar	0.00	0.00	0.0	0.0
11	AFG	Asia	Afghanistan	2020-03-06	5.0	0.0	0.000	0.0	0.0	0.000	0.126	0.000	0.000	0.000	0.000	0.000	0.00	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000	0.000	0.0	0.000	0.000	0.0	0	0.0	0.0	0.0	0.0	0.0	0.0	0.00	0.00	0.00	0.0	0.0	27.78	39835428.0	54.422	18.6	2.581	1.337	1803.987	0.0	597.029	9.59	0.0	0.0	37.746	0.5	64.83	0.511	0.0	2020	3	6	Friday	1	Mar	0.00	0.00	0.0	0.0
12	AFG	Asia	Afghanistan	2020-03-07	8.0	3.0	0.429	0.0	0.0	0.000	0.201	0.075	0.011	0.000	0.000	0.000	0.00	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000	0.000	0.0	0.000	0.000	0.0	0	0.0	0.0	0.0	0.0	0.0	0.0	0.00	0.00	0.00	0.0	0.0	27.78	39835428.0	54.422	18.6	2.581	1.337	1803.987	0.0	597.029	9.59	0.0	0.0	37.746	0.5	64.83	0.511	0.0	2020	3	7	Saturday	1	Mar	0.00	0.00	0.0	0.0
13	AFG	Asia	Afghanistan	2020-03-08	8.0	0.0	0.429	0.0	0.0	0.000	0.201	0.000	0.011	0.000	0.000	0.000	0.00	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000	0.000	0.0	0.000	0.000	0.0	0	0.0	0.0	0.0	0.0	0.0	0.0	0.00	0.00	0.00	0.0	0.0	27.78	39835428.0	54.422	18.6	2.581	1.337	1803.987	0.0	597.029	9.59	0.0	0.0	37.746	0.5	64.83	0.511	0.0	2020	3	8	Sunday	1	Mar	0.00	0.00	0.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
116762	ZWE	Africa	Zimbabwe	2021-09-10	126163.0	107.0	118.857	4532.0	11.0	10.714	8359.500	7.090	7.875	300.288	0.729	0.710	0.59	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2411.0	1180285.0	78.205	0.160	4185.0	0.277	0.028	35.2	tests performed	4656448.0	2824296.0	1832152.0	0.0	54428.0	40605.0	30.85	18.71	12.14	0.0	2690.0	0.00	15092171.0	42.729	19.6	2.822	1.882	1899.775	21.4	307.846	1.82	1.6	30.7	36.791	1.7	61.49	0.571	0.0	2021	9	10	Friday	81	Sep	3.59	0.84	19.0	12.0
116763	ZWE	Africa	Zimbabwe	2021-09-11	126220.0	57.0	113.571	4536.0	4.0	10.000	8363.277	3.777	7.525	300.553	0.265	0.663	0.60	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2166.0	1182451.0	78.349	0.144	4108.0	0.272	0.028	36.2	tests performed	4708905.0	2844848.0	1864057.0	0.0	52457.0	44094.0	31.20	18.85	12.35	0.0	2922.0	0.00	15092171.0	42.729	19.6	2.822	1.882	1899.775	21.4	307.846	1.82	1.6	30.7	36.791	1.7	61.49	0.571	0.0	2021	9	11	Saturday	81	Sep	3.59	0.84	19.0	12.0
116764	ZWE	Africa	Zimbabwe	2021-09-12	126269.0	49.0	102.714	4538.0	2.0	8.000	8366.523	3.247	6.806	300.686	0.133	0.530	0.60	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2035.0	1184486.0	78.483	0.135	3978.0	0.264	0.026	38.7	tests performed	0.0	0.0	0.0	0.0	0.0	42719.0	0.00	0.00	0.00	0.0	2831.0	0.00	15092171.0	42.729	19.6	2.822	1.882	1899.775	21.4	307.846	1.82	1.6	30.7	36.791	1.7	61.49	0.571	0.0	2021	9	12	Sunday	81	Sep	3.59	0.84	0.0	0.0
116765	ZWE	Africa	Zimbabwe	2021-09-13	126399.0	130.0	104.000	4543.0	5.0	7.143	8375.137	8.614	6.891	301.017	0.331	0.473	0.00	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000	0.000	0.0	0.000	0.000	0.0	0	4752356.0	2856655.0	1895701.0	0.0	0.0	41369.0	31.49	18.93	12.56	0.0	2741.0	0.00	15092171.0	42.729	19.6	2.822	1.882	1899.775	21.4	307.846	1.82	1.6	30.7	36.791	1.7	61.49	0.571	0.0	2021	9	13	Monday	82	Sep	3.59	0.84	19.0	13.0
116766	ZWE	Africa	Zimbabwe	2021-09-14	126817.0	418.0	145.857	4550.0	7.0	6.714	8402.833	27.696	9.664	301.481	0.464	0.445	0.00	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000	0.000	0.0	0.000	0.000	0.0	0	0.0	0.0	0.0	0.0	0.0	0.0	0.00	0.00	0.00	0.0	0.0	0.00	15092171.0	42.729	19.6	2.822	1.882	1899.775	21.4	307.846	1.82	1.6	30.7	36.791	1.7	61.49	0.571	0.0	2021	9	14	Tuesday	82	Sep	3.59	0.84	0.0	0.0

114675 rows × 72 columns

Insert country name

country = 'Poland'

df_country = df[df['location'] == country]

fig = px.line(df_country, x='date', y='fatality',
                title=f'Fatality During Covid-19 Outbreak in {country}',
                labels={'date': 'Date', 'fatality':'Fatality[%]'})
fig

Vacinations

df_vac = df_country[df_country['vacinated'] != 0]


fig = go.Figure()
fig.add_trace(go.Line(x=df_vac['date'], y=df_vac['vacinated'],
                     name='People vacinated [%]'))
fig.add_trace(go.Line(x=df_vac['date'], y=df_vac['vacinated_fully'],
                     name='People vacinated fully [%]'))
fig.update_layout(
    title_text=f"Vacinations in {country}")

fig.update_xaxes(title_text ='Date')
fig.update_yaxes(title_text ='Percentage ')

fig

df_vacinated = df.groupby('location')
df_vacinated = df_vacinated[['vacinated','vacinated_fully']].max()
df_vacinated = df_vacinated[df_vacinated['vacinated'] < 100]

fig = make_subplots(specs=[[{"secondary_y": True}]])

df_hosp = df_country[df_country['hosp_patients'] != 0]

fig.add_trace(go.Line(x=df_hosp['date'], y=df_hosp['hosp_patients'],
                     name='Hospitialized Patients'),secondary_y=False)
fig.add_trace(go.Line(x=df_hosp['date'], y=df_hosp['stringency_index'],
                     name='Stringency index'), secondary_y=True)
fig.update_layout(
    title_text=f"Restriction in {country}")

fig.update_xaxes(title_text ='Date')
fig.update_yaxes(title_text ='Stringency index',secondary_y=True,
                title_font=dict(color='red'))
fig.update_yaxes(title_text ='Hospitalized people',secondary_y=False,
                title_font=dict(color='blue'))
fig.update_layout(showlegend=False)

fig

fig = make_subplots(specs=[[{"secondary_y": True}]])

fig.add_trace(go.Line(x=df_country['date'], y=df_country['reproduction_rate'],
                     name='Reproduction rate'), secondary_y = True)
fig.add_trace(go.Line(x=df_country['date'], y=df_country['new_cases_smoothed'],
                     name='New Cases'), secondary_y = False)
fig.update_layout(title_text=f'Reproduction Rate in {country}')

fig.update_yaxes(title_text ='Reproduction Rate',secondary_y=True,
                title_font=dict(color='blue'))
fig.update_yaxes(title_text ='New Cases',secondary_y=False,
                title_font=dict(color='red'))
fig.update_layout(showlegend=False)

fig = make_subplots(specs=[[{"secondary_y": True}]])


fig.add_trace(go.Line(x=df_country['date'], y=df_country['infected'],
                     name='% Infected Society'), secondary_y = True)
fig.add_trace(go.Line(x=df_country['date'], y=df_country['new_cases_smoothed'],
                     name='New Cases'),secondary_y = False)
fig.add_trace(go.Line(x=df_country['date'], y=df_country['new_tests_smoothed'],
                     name='New Tests'),secondary_y = False)
fig.update_layout(
    title_text=f"The course of the Covid-19 pandemic in {country}")

fig.update_xaxes(title_text ='Date')

fig.update_yaxes(title_text = '% <b>Infected</b>', secondary_y = True)
fig.update_yaxes(title_text = '<b>Quantity</b>', secondary_y = False)

best_vacinated = df_vacinated.nlargest(17,'vacinated_fully')
countries_vac = best_vacinated.index
fig = go.Figure()

fig.add_trace(go.Bar(x = countries_vac, y = best_vacinated.vacinated_fully,
                    name = 'With a full dose',
                    marker_color = 'steelblue'))

fig.add_trace(go.Bar(x = countries_vac, y = best_vacinated.vacinated,
                    name = 'At least one dose',
                    marker_color ='lightskyblue'))

fig.update_layout(title_text = "Best vaccinating countries",
                  legend_title_text='Percentage vaccinated %')
    
fig

Preapering Countries DF

countries_sum = df.groupby(['iso_code']).sum()
countries_mean = df.groupby(['iso_code']).mean()
countries_med = df.groupby(['iso_code']).median()

countries_sum = countries_sum.drop(columns=['day', 'month', 'year','total_deaths','total_cases_per_million','total_deaths_per_million',
                                            'reproduction_rate','extreme_poverty','gdp_per_capita','aged_70_older','aged_65_older',
                                           'median_age','population_density','fatality','stringency_index','icu_patients','icu_patients_per_million',
                                           'population','handwashing_facilities','human_development_index','people_fully_vaccinated_per_hundred',
                                           'life_expectancy'])
iso = countries_sum.index.tolist()

countries_last = df.drop_duplicates(subset=['iso_code'],keep='last')
countries_last = countries_last[countries_last['continent'] != 0]

columns = df.columns

Select continent

continent = 'Europe'

How to use the chart below?

• Double-click on the country name in the legend to see a single country.
• Single click selects/unselects a country

df_EU = df[df['continent'] == continent]

fig = px.line(df_EU, x='date', y='new_cases_smoothed', 
                     color='location',
             title=f'Covid-19 New Cases in {continent}')
fig.update_yaxes(title_text = 'New Cases')
fig.update_xaxes(title_text = 'Date')
fig.update_xaxes(rangeslider_visible=True)

animation_frame = df_EU.date.astype(str)

fig = px.bar(x=df_EU['location'], y=df_EU['total_cases'], animation_frame=animation_frame, color=df_EU['fatality'],
            title=f'The course of a pandemic in {continent} - Color represents mortality[%]')
fig.update_yaxes(title_text = 'Total Cases')
fig.show()

df_sum_per_day = df.groupby("date").sum()
df_sum_per_day = pd.merge(df_sum_per_day.reset_index(),
                         df[['date','day_name', 'week_nr','month_name']],
                         on='date',
                         how='left')

fig = go.Figure(data=go.Heatmap( 
        z=df_sum_per_day.new_cases,
        x=df_sum_per_day.day_name,
        y=df_sum_per_day.month_name,
        colorscale='temps'))
fig.update_layout(title="Heatmap of New Cases during the COVID-19 pandemic")
fig.show()

Coordinates of each country

from geopy.geocoders import Nominatim

geolocator = Nominatim(user_agent = 'mStachowicz')
def geolocate(country):
    """Geolocace the center of the country
    Args:
        country (str): Country name
    Return:
        (lat, lon) (float): Latitude and Longitude
    
    """
    try:
        loc = geolocator.geocode(country)
        # And return latitude and longitude
        return (loc.latitude, loc.longitude)
    except:
        print("Country not found!")
        return np.nan

countries_last['coordinates'] = countries_last['location'].apply(geolocate)
countries_last['lat'] = countries_last['coordinates'].apply(lambda x: x[0])
countries_last['lon'] = countries_last['coordinates'].apply(lambda x: x[1])

def human_format(num: float) -> str:
    """Writes a number using suffixes
    Args:
        number (str): Country name
    Returns:
        human format number (str)
    """
    
    magnitude = 0
    while abs(num) >= 1000:
        magnitude += 1
        num /= 1000.0
    return '%.2f%s' % (num, ['', 'K', 'M', 'G', 'T', 'P'][magnitude])

countries_last['total_cases_prettify'] = [ human_format(x) for x in countries_last['total_cases']]
countries_last['total_deaths_prettify'] = [ human_format(x) for x in countries_last['total_deaths']]

# Prepering markers
countries_last['summary'] = (
    '<b>'
    + 'Country: '
    + countries_last['location']
    + '</b>'
    + '<br><br>'
    + 'Confirmed: '
    + countries_last['total_cases_prettify'].astype(str)
    + '<br>'
    + 'Deaths: '
    + countries_last['total_deaths_prettify'].astype(str)
    + '<br>'
    + 'Fatality: '
    + countries_last['fatality'].astype(str)
    + "%")

countries_last['fatality'] = countries_last['fatality'].apply(lambda x: round(x,2)) 

fig = go.Figure()

fig.add_trace(go.Scattergeo(
            lat = countries_last.lat,
            lon = countries_last.lon,
            text = countries_last.summary,
            marker = dict(
                size = countries_last.total_cases / 200000,
                color = countries_last.fatality,
                opacity = 0.8))
             )
fig.update_layout(title_text = 'Current status of the epidemic - color represents fatality')
fig.update_geos(showcountries=True)

fig = go.Figure(data=go.Choropleth(
                locations=iso,
                z = countries_mean['fatality'],
                locationmode='ISO-3',
                colorscale="sunset"
                ))
fig.update_layout(title_text ='Mean fatality During Covid-19 outbreak') 
fig.update_layout(legend_title_text = "Fatality[%]")

animation_frame = df.date.astype(str)

px.choropleth(df,
              locations='iso_code',
              color='total_cases',
              animation_frame=animation_frame,
              animation_group='location',
              locationmode='ISO-3',
              color_continuous_scale="dense",
              range_color=(0, 30000000),
              title = "Course of the Covid-19 epidemic")              

# Saving database to excel
countries_last.to_excel('countries_database.xlsx')

countries_plot = countries_last[countries_last['total_deaths'] != 0]

countries_plot

fig = px.sunburst(countries_plot, path=['continent', 'location'], values='total_deaths',
                  color='hospital_beds_per_thousand', hover_data=['iso_code'],
                 title='Distribution of all deaths',
                 color_continuous_scale=["red", "yellow", "green"])
fig.show()

fig = px.treemap(countries_plot, path=[px.Constant('world'), 'continent', 'location'], values='total_deaths',
                  color='aged_65_older', hover_data=['total_deaths'],
                title='Total Deaths depend on % people over 65 years old')
fig.show()

What deaths are correlated with ?

df_cov = countries_plot.cov()
df_cov
df_cov['total_deaths'].nlargest(15)

population                        5.366915e+12
total_tests                       1.293948e+12
total_cases                       3.198286e+11
total_deaths                      6.366684e+09
new_tests_smoothed                3.835412e+09
new_tests                         3.457396e+09
new_cases_smoothed                7.163308e+08
total_cases_per_million           6.597328e+08
new_cases                         6.562804e+08
gdp_per_capita                    1.528388e+08
total_deaths_per_million          2.577867e+07
new_deaths                        1.069128e+07
new_deaths_smoothed               1.058307e+07
total_tests_per_thousand          7.443680e+05
new_cases_smoothed_per_million    1.402597e+05
Name: total_deaths, dtype: float64